{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 挑战 26：各国 CO2 排放量与 GDP 总值关联分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 数据清洁"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_data = pd.read_excel(\"ClimateChange.xlsx\", sheet_name='Data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 选择数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_co2 = df_data[df_data['Series code'] =='EN.ATM.CO2E.KT'].set_index('Country code')\n",
    "df_gdp = df_data[df_data['Series code'] =='NY.GDP.MKTP.CD'].set_index('Country code')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失数据替换并填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 替换\n",
    "df_co2_nan = df_co2.replace({'..': pd.np.NaN})\n",
    "df_gdp_nan = df_gdp.replace({'..': pd.np.NaN})\n",
    "\n",
    "# 填充\n",
    "df_co2_fill = df_co2_nan.iloc[:, 5:].fillna(method='ffill', axis=1).fillna(method='bfill', axis=1)\n",
    "df_gdp_fill = df_gdp_nan.iloc[:, 5:].fillna(method='ffill', axis=1).fillna(method='bfill', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_co2_fill['CO2-SUM'] = df_co2_fill.sum(axis=1)\n",
    "df_gdp_fill['GDP-SUM'] = df_gdp_fill.sum(axis=1)\n",
    "df_merge = pd.concat([df_co2_fill['CO2-SUM'], df_gdp_fill['GDP-SUM']], axis=1)\n",
    "\n",
    "df_clean = df_merge.fillna(value=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 数据整理及绘图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$X^{*}=\\frac{X-X_{min}}{X_{max}-X_{min}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_max_min = (df_clean - df_clean.min()) / (df_clean.max() - df_clean.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 获取中国归一化后的 CO2 和 GDP 数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "china = []\n",
    "for i in df_max_min[df_max_min.index == 'CHN'].values:\n",
    "    china.extend(np.round(i, 3).tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 坐标刻度处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取 5 个常任理事国标签及对应的坐标刻度\n",
    "countries_labels = ['USA', 'CHN', 'FRA', 'RUS', 'GBR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取国家标签作为刻度标签\n",
    "sticks_labels = []\n",
    "\n",
    "# 获取相应国家序号对应着刻度坐标\n",
    "labels_position = []\n",
    "\n",
    "for i in range(len(df_max_min)):\n",
    "    if df_max_min.index[i] in countries_labels:\n",
    "        sticks_labels.append(df_max_min.index[i])\n",
    "        labels_position.append(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对数据进行绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x11c3813c8>,\n",
       "  <matplotlib.axis.XTick at 0x11a36cc88>,\n",
       "  <matplotlib.axis.XTick at 0x11a36c978>,\n",
       "  <matplotlib.axis.XTick at 0x11c3cf4a8>,\n",
       "  <matplotlib.axis.XTick at 0x11c3cfba8>],\n",
       " <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "df_max_min.plot(kind='line', title='GDP-CO2', figsize=(12,6))\n",
    "\n",
    "plt.xlabel(\"Countries\")\n",
    "plt.ylabel(\"Values\")\n",
    "\n",
    "# 绘制 5 大常任理事国坐标刻度标签\n",
    "plt.xticks(labels_position, sticks_labels, rotation='vertical')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
